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Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

Image representation and approximate query is always a research challenge and is affected greatly by the dimension and size of images. Since hash-based methods and binary encodings in combination with other techniques, such as kernel tricks, a longer binary code and mapping vectors rotation, can maintain a linear query time and query accuracy, they have been used in this area broadly. This paper develops principal component analysis hashing (PCAH) and unequal length of binary coding to divide images into more categories, denoted as PCA-MD, to improve accuracy of the representation and lookup of images. This paper firstly proves that the eigenvector mapping is locality sensitive, which is the basis for more classes division. For the anisotropy of the eigenvectors, PCA-MD utilizes an unequal length of binary coding and fewer eigenvectors, rather than an equal code, to divide the images mapped on every eigenvector to more categories. Moreover, L1-norm distance is applied to measure the distances of images to avoid the enormous computation of Euclidean distance. Theoretical analysis and extensive experimental results demonstrate that the PCA-MD has a higher query performance and a slight longer run time than the state-of-the-art approaches based on the Hamming distance. This in turn verifies that PCAH is a locality sensitive hash and that partitioning into more categories rather than only two categories is reasonable.

With the rapid increase of vehicle ownership in China, toll collection stations on expressways have become some of the most congested areas. Compared with a manual toll collection system (M...

With the further liberalization of the electricity market of China, customers’ requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackout-sensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers.

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

In this paper, the electric field resonance (EFR) method, similar to the four-coil configuration of the magnetic field resonance wireless power transfer, is proposed for the capacitive coupling power transfer. The characteristics of the proposed method are derived and analyzed. With the EFR method, not only unity power factor for the power source is achieved, but also high power factor and low reactive power for the capacitive coupling stage are achieved. Effective power transfer is realized by the EFR method. Based on the proposed method, a dynamic charging concept for railway vehicles is then proposed. A prototype powering system is designed and built to prove the validity of the proposed method. Analytical, simulation, and experimental results are given and compared. A 23-cm model vehicle is put on a 150-cm track. It is shown that about 700-W power is transferred through a 24-pF coupling capacitor. The proposed method reaches 91% dc–dc overall efficiency at switching frequency 2 MHz.

In order to improve the compression ratio, most of improved compression methods require more memory and CPU time. But these improvements are not suitable for embedded system with limited-resource, especially for vehicle communications system. In order to communicate realtimely by lower costs, the huge and short data are compressed before transmitting. This paper discusses several original lossless compression methods including their principles, features, advantages and disadvantages respectively. Experiments on the front vehicle system show that the LZ77, PPM and BWT are more suitable for the automobile communication data, and they have better compression ratios, simpler coding, lower CPU and memory costs.

Wireless power transfer (WPT) has attracted widely attention in the last decade, especially in implantable medical devices, electrical vehicle battery charging, the mining equipment, and so on. In a WPT system, the two most adopted structures are two-coil and four-coil configurations. Actually, the four-coil can be equivalent to the two-coil configuration under certain conditions. In this paper, a four-coil structure is equivalent to two different types of two-coil structure at the resonant frequency. The analytical expressions of the equivalent are derived. The simulation and experimental results verify the effectiveness of the equivalent.

Network attack path analysis is an important method for analyzing the security status of computer network, which can automatically analyze the correlation between network vulnerabilities and potential threats resulting from vulnerabilities. It plays a guiding role in establishing network security policy. This paper chooses NVD and Bugtraq as vulnerability data sources, and extracts key properties required to build a vulnerability database that mainly contains privilege escalation vulnerabilities in Linux system and common server software. An association analysis of vulnerabilities and related information is made and properties are abstracted to construct atomic attacks and corresponding atomic attack database. A network attack model is constructed from network connection and host configuration. Via matching atomic attacks in attack database, the paper adopts state comparison algorithm to mine potential attack paths that may lead to specified attack goals. The experiment verifies that the proposed approach can reduce the number of attack states effectively and mine all non-redundant attack paths.

In order to represent the knowledge model of the combination of time and actions in a possible world,this article proposes a knowledge representation(KR) method which is based on first order logic and its semantic can be interpreted by production rules. First,we discuss the basic theory of knowledge representation(KR), and next,we construct a meta language through which semantic can be explained by the rules of semantic interpretation and predicates, and then,by the meta language and its semantic we describe the concepts in the possible world. At last,we use experiments in the possible world to demonstrate the effectiveness of the presented method.

Non-parametric statistical methods are applied to verdict that early failure behavior of the testing process may have less impact on later failure process, so it happens in software failure time prediction that one does not have enough information to estimate the software failure process well but do have enough information to estimate the failure data at given instance. The prediction accuracy of software reliability prediction models based on recurrent neural network, feed-forward neural network, relevance vector machine, support vector machine and some nonhomogeneous Poisson process models is compared. Experimental results show that software failure time prediction models based on transduction inference theory could achieve higher prediction accuracy.